Cu/TiO2 adsorbents modified by air plasma for adsorption-oxidation of H2S.

In this study, non-thermal plasma (NTP) was employed to modify the Cu/TiO2 adsorbent to efficiently purify H2S in low-temperature and micro-oxygen environments. The effects of Cu loading amounts and atmospheres of NTP treatment on the adsorption-oxidation performance of the adsorbents were investigated. The NTP modification successfully boosted the H2S removal capacity to varying degrees, and the optimized adsorbent treated by air plasma (Cu/TiO2-Air) attained the best H2S breakthrough capacity of 113.29 mg H2S/gadsorbent, which was almost 5 times higher than that of the adsorbent without NTP modification. Further studies demonstrated that the superior performance of Cu/TiO2-Air was attributed to increased mesoporous volume, more exposure of active sites (CuO) and functional groups (amino groups and hydroxyl groups), enhanced Ti-O-Cu interaction, and the favorable ratio of active oxygen species. Additionally, the X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) results indicated the main reason for the deactivation was the consumption of the active components (CuO) and the agglomeration of reaction products (CuS and SO42-) occupying the active sites on the surface and the inner pores of the adsorbents.

Focusing on the Immune Cells: Recent Advances in Immunotherapy for Biliary Tract Cancer.

Biliary tract cancer (BTC) represents a challenging malignancy characterized by aggressive behavior, high relapse rates, and poor prognosis. In recent years, immunotherapy has revolutionized the treatment landscape for various cancers, but its efficacy in BTC remains limited. This article provides a comprehensive overview of the advances in preclinical and clinical studies of immunotherapy for BTC. We explore the potential of immune checkpoint inhibitors in reshaping the management of BTC. Despite disappointing results thus far, ongoing clinical trials are investigating the combination of immunotherapy with other treatment modalities. Furthermore, research on the tumor microenvironment has unveiled novel targets for immunotherapeutic interventions. By understanding the current state of immunotherapy in BTC and highlighting future directions, this article aims to fuel further exploration and ultimately improve patient outcomes in this challenging disease.

The Mediating Effect of Social Participation Restriction on the Association Between Role Overload and Mental Health Among Caregivers of Older Adults with Dementia.

Caregivers of older adults with dementia (dementia caregivers) often experience high role overload (i.e., feeling overwhelmed by caregiving demands that exceed available resources), which can take a toll on their mental health. Moreover, dementia caregiving can restrict caregivers' participation in valued social activities. This study aims to examine the mediating effect of social participation restriction on the relationship between role overload and mental health among dementia caregivers. A total of 894 dementia caregivers (mean age = 61.77) were selected from the National Study of Caregiving. The sample was predominantly women (64%) and White (78%). Path analysis shows that social participation restriction partially mediated the associations between (1) role overload and psychological distress (indirect effect ß = .08, p < .001) and (2) role overload and psychological wellbeing (indirect effect ß = -.05, p < .05). Interventions targeting coping strategies and healthcare-recreation programs are needed to improve social participation among dementia caregivers.

LcMPK3 and LcMPK6 positively regulate fruitlet abscission in litchi.

Mitogen-activated protein kinase (MAPK) cascades have been discovered to play a fundamental role in regulating organ abscission. However, the identity of protein substrates targeted by MAPK cascades, as well as whether the role of MAPK protein cascades in the abscission process is conserved across different plant species, remain unknown. Here, the role of homologs of MPK3 and MPK6 in regulating fruit abscission were characterized in litchi. Ectopic expression of LcMPK3 or LcMPK6 in Arabidopsis mpk3 mpk6 mutant rescued the deficiency in floral organ abscission, while silencing of LcMPK3 or LcMPK6 in litchi significantly decreased fruitlet abscission. Importantly, a total of 49 proteins interacting with LcMPK3 were identified through yeast two-hybrid screening, including two components of the MAPK signaling cascade, five transcription factors, and two aquaporins. Furthermore, the interaction between LcMPK3/6 with LcBZR1/2, core components in brassinosteroids signaling that suppress litchi fruitlet abscission, was confirmed using in vitro and in vivo assays. Moreover, phos-tag assays demonstrated that LcMPK3/6 could phosphorylate LcBZR1/2, with several phosphorylation residues identified. Together, our findings suggest that LcMPK3 and LcMPK6 play a positive regulatory role in fruitlet abscission in litchi, and offer crucial information for the investigation of mechanisms underlying MPK3/6-mediated organ abscission in plants.

Rigdelet neural network and improved partial reinforcement effect optimizer for music genre classification from sound spectrum images.

In this paper, a new approach has been introduced for classifying the music genres. The proposed approach involves transforming an audio signal into a unified representation known as a sound spectrum, from which texture features have been extracted using an enhanced Rigdelet Neural Network (RNN). Additionally, the RNN has been optimized using an improved version of the partial reinforcement effect optimizer (IPREO) that effectively avoids local optima and enhances the RNN's generalization capability. The GTZAN dataset has been utilized in experiments to assess the effectiveness of the proposed RNN/IPREO model for music genre classification. The results show an impressive accuracy of 92 % by incorporating a combination of spectral centroid, Mel-spectrogram, and Mel-frequency cepstral coefficients (MFCCs) as features. This performance significantly outperformed K-Means (58 %) and Support Vector Machines (up to 68 %). Furthermore, the RNN/IPREO model outshined various deep learning architectures such as Neural Networks (65 %), RNNs (84 %), CNNs (88 %), DNNs (86 %), VGG-16 (91 %), and ResNet-50 (90 %). It is worth noting that the RNN/IPREO model was able to achieve comparable results to well-known deep models like VGG-16, ResNet-50, and RNN-LSTM, sometimes even surpassing their scores. This highlights the strength of its hybrid CNN-Bi-directional RNN design in conjunction with the IPREO parameter optimization algorithm for extracting intricate and sequential auditory data.

ASB1 inhibits prostate cancer progression by destabilizing CHCHD3 via K48-linked ubiquitination.

Prostate cancer is a major contributor to male mortality worldwide. In this study, we revealed that Ankyrin Repeat and SOCS Box Containing 1 (ASB1) expression was significantly decreased in prostate cancer tissues, correlating strongly with poor patient prognosis. Notably, the group with low ASB1 expression exhibited an increased proportion of M2 macrophages and showed resistance to immune checkpoint inhibitors and cisplatin, but remained sensitive to androgen-receptor-targeting drug bicalutamide. Silencing ASB1 enhanced prostate cancer cell proliferation, clonogenicity, and migration, whereas its overexpression exerted the opposite effects. Through quantitative mass spectrometry interactome analysis, we identified 37 novel proteins interacting with ASB1, including CHCHD3. Subsequent experiments including co-immunoprecipitation, cycloheximide treatment, and ubiquitination assays, revealed that ASB1 interacts with CHCHD3, promoting its degradation via K48-linked ubiquitination. Cell rescue experiments further demonstrated that ASB1 inhibits prostate cancer cell through the CHCHD3/reactive oxygen species (ROS) pathway. Taken together, our study indicated that ASB1 functions as a tumor suppressor by inhibiting CHCHD3/ROS signaling, thereby playing a vital part in prevention of prostate cancer proliferation, clonogenicity, and migration.

Genomic analysis and metabolic characteristics provide insights into inorganic nitrogen metabolism of novel bacterium Acinetobacter pittii J08.

A novel A. pittii J08 with heterotrophic nitrification and aerobic denitrification (HN-AD) isolated from pond sediments could rapidly degrade inorganic nitrogen (N) and total nitrogen (TN-N) with ammonium (NH4+-N) preference. N degradation rate of NH4+-N, nitrite (NO2--N) and nitrate (NO3--N) were 3.9 mgL-1h-1, 3.0 mgL-1h-1 and 2.7 mgL-1h-1, respectively. In addition, strain J08 could effectively utilize most of detected low-molecular-weight carbon (LMWC) sources to degrade inorganic N with a wide adaptability to various culture conditions. Whole genome sequencing (WGS) analysis revealed that assembled genome of stain J08 possessed the crucial genes involved in dissimilatory/assimilatory NO3--N reduction and NH4+-N assimilation. These results indicated that strain J08 could be applied to wastewater treatment in aquaculture.

Deep Learning in Gene Regulatory Network Inference: A Survey.

Understanding the intricate regulatory relationships among genes is crucial for comprehending the development, differentiation, and cellular response in living systems. Consequently, inferring gene regulatory networks (GRNs) based on observed data has gained significant attention as a fundamental goal in biological applications. The proliferation and diversification of available data present both opportunities and challenges in accurately inferring GRNs. Deep learning, a highly successful technique in various domains, holds promise in aiding GRN inference. Several GRN inference methods employing deep learning models have been proposed; however, the selection of an appropriate method remains a challenge for life scientists. In this survey, we provide a comprehensive analysis of 12 GRN inference methods that leverage deep learning models. We trace the evolution of these major methods and categorize them based on the types of applicable data. We delve into the core concepts and specific steps of each method, offering a detailed evaluation of their effectiveness and scalability across different scenarios. These insights enable us to make informed recommendations. Moreover, we explore the challenges faced by GRN inference methods utilizing deep learning and discuss future directions, providing valuable suggestions for the advancement of data scientists in this field.

Gegen Qinlian Decoction Combined with Conventional Western Medicine for the Treatment of Infectious Diarrhea: A Systematic Review and Trial Sequential Analysis.

BACKGROUND: Infectious diarrhea (ID) is a highly prevalent disease worldwide that poses a substantial risk to human well-being. In China, numerous clinical studies have investigated the efficacy of Gegen Qinlian Decoction (GGQLD) in treating ID. However, there is a need for additional rigorous and evidence-based medical research to enhance physicians' confidence in their prescribing practices. METHODS: Seven Chinese and English databases were systematically searched. The Cochrane Risk of Bias tool was used to assess the quality of the included studies. Meta-analysis was conducted using RevMan 5.3, and Stata 16.0 was used for the sensitivity analysis. Trial sequential analysis was performed using TSA v0.9, and GRADEprofiler was utilized to evaluate the quality of evidence. RESULTS: A total of 12 randomized controlled trials (RCTs) involving 1240 patients were included. The meta-analysis demonstrated that the combination of GGQLD with conventional Western medicine had better effects on clinical efficacy (RR=1.15, 95% CI [1.10, 1.20]), duration of diarrhea symptoms (WMD=-10.96, 95% CI [-11.97, -9.96]), duration of abdominal pain symptoms (WMD=-12.01, 95% CI [-14.12, -9.90]), duration of fever symptoms (WMD=-11.91, 95% CI [-13.39, -10.43]), interleukin-6 (IL-6) levels (WMD=-113.59, 95% CI [-113.03, -108.14]), and tumor necrosis factor-α (TNF-α) levels (WMD=-62.18, 95% CI[-65.25, -59.11]) and that no significant adverse reactions occurred (RR = 0.45, 95% CI [0.10, 1.97]). The sample size of the included studies reached the expected size. The quality of evidence for outcome indicators was rated as low or very low. CONCLUSIONS: The combination of GGQLD with conventional Western medicine demonstrates promising efficacy and safety in treating ID. Nonetheless, more high-quality RCTs are required to confirm this conclusion.

Harnessing risk assessment for thrombosis and bleeding to optimize anticoagulation strategy in nonvalvular atrial fibrillation.

BACKGROUND: Oral anticoagulation (OAC) following catheter ablation (CA) of nonvalvular atrial fibrillation (NVAF) is essential for the prevention of thrombosis events. Inappropriate application of OACs does not benefit thrombosis prevention but may be associated with a higher risk of bleeding. Therefore, this study aims to develop clinical data-driven machine learning (ML) methods to predict the risk of thrombosis and bleeding to establish more precise anticoagulation strategies for patients with NVAF. METHODS: Patients with NVAF underwent CA therapy were enrolled from Southwest Hospital from 2015 to 2023. This study compared eight ML algorithms to evaluate the predictive power for both thrombosis and bleeding. Model interpretations were recognized by feature importance and SHapley Addictive exPlanations methods. With potential essential risk factors, simplified ML models were proposed to improve the feasibility of the tool. RESULTS: A total of 1055 participants were recruited, including 105 patients with thrombosis and 252 patients with bleeding. The models based on XGBoost achieved the best performance with accuracies of 0.704 and 0.781 for thrombosis and bleeding. Age, BNP and the duration of heparin and are closely related to the high risk of thrombosis, whereas anticoagulation strategy, BNP and lipids play a crucial role in the occurrence of bleeding. The optimized models enrolling crucial risk factors, RF-T for thrombosis and Xw-B for bleeding, achieved the best recalls of 0.774 and 0.780, respectively. CONCLUSIONS: The optimized models will have a great clinical application in predicting thrombosis and bleeding among NVAF patients and will form the basis for future score scales.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies.

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Regulation and Therapeutic Application of Long non-Coding RNA in Tumor Angiogenesis.

Tumor growth and metastasis rely on angiogenesis. In recent years, long non-coding RNAs have been shown to play an important role in regulating tumor angiogenesis. Here, we review the multidimensional modes and relevant molecular mechanisms of long non-coding RNAs in regulating tumor angiogenesis. In addition, we summarize new strategies for tumor anti-angiogenesis therapies by targeting long non-coding RNAs. The aim of this study is to provide new diagnostic targets and treatment strategies for anti-angiogenic tumor therapy.

Identification and experimental verification of a biomarker by combining the unfolded protein response with the immune cells in colon cancer.

BACKGROUND: The unfolded protein response (UPR) is associated with immune cells that regulate the biological behavior of tumors. This article aims to combine UPR-associated genes with immune cells to find a prognostic marker and to verify its connection to the UPR. METHODS: Univariate cox analysis was used to screen prognostically relevant UPRs and further screened for key UPRs among them by machine learning. ssGSEA was used to calculate immune cell abundance. Univariate cox analysis was used to screen for prognostically relevant immune cells. Multivariate cox analysis was used to calculate UPR_score and Tumor Immune Microenvironment score (TIME_score). WGCNA was used to screen UPR-Immune-related (UI-related) genes. Consensus clustering analysis was used to classify patients into molecular subtype. Based on the UI-related genes, we classified colon adenocarcinoma (COAD) samples by cluster analysis. Single-cell analysis was used to analyze the role of UI-related genes. We detected the function of TIMP1 by cell counting and transwell. Immunoblotting was used to detect whether TIMP1 was regulated by key UPR genes. RESULTS: Combined UPR-related genes and immune cells can determine the prognosis of COAD patients. Cluster analysis showed that UI-related genes were associated with clinical features of COAD. Single-cell analysis revealed that UI-related genes may act through stromal cells. We defined three key UI-related genes by machine learning algorithms. Finally, we found that TIMP1, regulated by key genes of UPR, promoted colon cancer proliferation and metastasis. CONCLUSIONS: We found that TIMP1 was a prognostic marker and experimentally confirmed that TIMP1 was regulated by key genes of UPR.

The immunomodulatory effects of cannabidiol on Hsp70-activated NK cells and tumor target cells.

BACKGROUND: Cannabidiol (CBD), the major non-psychoactive component of cannabis, exhibits anti-inflammatory properties, but less is known about the immunomodulatory potential of CBD on activated natural killer (NK) cells and/or their targets. Many tumor cells present heat shock protein 70 (Hsp70) on their cell surface in a tumor-specific manner and although a membrane Hsp70 (mHsp70) positive phenotype serves as a target for Hsp70-activated NK cells, a high mHsp70 expression is associated with tumor aggressiveness. This study investigated the immuno-modulatory potential of CBD on NK cells stimulated with TKD Hsp70 peptide and IL-2 (TKD+IL-2) and also on HCT116 p53wt and HCT116 p53-/- colorectal cancer cells exhibiting high and low basal levels of mHsp70 expression. RESULTS: Apart from an increase in the density of NTB-A and a reduced expression of LAMP-1, the expression of all other activatory NK cell receptors including NKp30, NKG2D and CD69 which are significantly up-regulated after stimulation with TKD+IL-2 remained unaffected after a co-treatment with CBD. However, the release of major pro-inflammatory cytokines by NK cells such as interferon-γ (IFN-γ) and the effector molecule granzyme B (GrzB) was significantly reduced upon CBD treatment. With respect to the tumor target cells, CBD significantly reduced the elevated expression of mHsp70 but had no effect on the low basal mHsp70 expression. Expression of other NK cell ligands such as MICA and MICB remained unaffected, and the NK cell ligands ULBP and B7-H6 were not expressed on these target cells. Consistent with the reduced mHsp70 expression, treatment of both effector and target cells with CBD reduced the killing of high mHsp70 expressing tumor cells by TKD+IL-2+CBD pre-treated NK cells but had no effect on the killing of low mHsp70 expressing tumor cells. Concomitantly, CBD treatment reduced the TKD+IL-2 induced increased release of IFN-γ, IL-4, TNF-α and GrzB, but CBD had no effect on the release of IFN-α when NK cells were co-incubated with tumor target cells. CONCLUSION: Cannabidiol (CBD) may potentially diminish the anti-tumor effectiveness of TKD+IL-2 activated natural killer (NK) cells.

The link between patient safety competence and adverse event among master of nursing students: a cross-sectional mixed-methods study.

BACKGROUND: Patient safety (PS) is a core competency for registered nurses. However, there is a gap between the PS competence of nursing students and their clinical experience in PS. This study explored the effect of PS competence levels on the occurrence of adverse events (AEs) among nursing master's students in China. METHODS: A sequential mixed methods design was used, with a purposive sample across seven colleges. A total of 327 graduate nursing students, aged 22 to 38, participated in the survey, and 15 participated in qualitative interviews. The Health Professional Education in Patient Safety Survey (H-PEPSS) assessed the students' competence levels in PS. The respondents also reported any AEs that they had been involved in over the past year. RESULTS: A total of 78 AEs occurred in the past year, with 17.7% of the participants involved 1 to 3 AEs. The most common AEs were medication administration errors (30.77%) and improper use of medical equipment/supplies (28.20%). Students acquired more competencies from the clinical setting than from the classroom setting. Three competencies learned from classroom settings were associated with clinical AEs: low clinical safety skills [OR = 0.61], inappropriate identify, response to and disclosing AE and close calls [OR = 0.454], and low confidence in working in teams with other health professionals [OR = 2.168]. Qualitative data analysis revealed five themes: recognizing AEs, reducing harm by addressing immediate risks to patients and others involved, promoting safe medication and clinical practice, managing members' authority and team dynamics, and dealing with inter-professional conflict. CONCLUSIONS: The quantitative and qualitative data align, supporting the enhancement of students' PS competence.

A machine learning model utilizing delphian lymph node characteristics to predict contralateral central lymph node metastasis in papillary thyroid carcinoma: A prospective multicenter study.

BACKGROUND: This study aimed to use artificial intelligence (AI) to integrate various radiological and clinical pathological data to identify effective predictors of contralateral cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC) and to establish a clinically applicable model to guide the extent of surgery. METHODS: This prospective cohort study included 603 patients with PTC from three centers. Clinical, pathological, and ultrasonographic data were collected and utilized to develop a machine learning (ML) model for predicting CCLNM. Model development at the internal center utilized logistic regression along with other ML algorithms. Diagnostic efficacy was compared among these methods, leading to the adoption of the final model (random forest). This model was subject to AI interpretation and externally validated at other centers. RESULTS: CCLNM was associated with multiple pathological factors. The Delphian lymph node metastasis ratio, ipsilateral cervical lymph node metastasis number, and presence of ipsilateral cervical lymph node metastasis were independent risk factors for CCLNM. Following feature selection, a Delphian lymph node-CCLNM (D-CCLNM) model was established using the Random forest algorithm based on five attributes. The D-CCLNM model demonstrated the highest area under the curve (AUC; 0.9273) in the training cohort and exhibited high predictive accuracy, with AUCs of 0.8907 and 0.9247 in the external and validation cohorts, respectively. CONCLUSIONS: We developed a new, effective method that uses ML to predict CCLNM in patients with PTC. This approach integrates data from Delphian lymph nodes and clinical characteristics, offering a foundation for guiding surgical decisions, and is conveniently applicable in clinical settings.

A Minimalist Method for Fully Oxygen-Tolerant RAFT Polymerization through Sulfur-Centered Trithiocarbonate Radical Initiation.

In recent years, the fully oxygen-tolerant reversible deactivation radical polymerization (RDRP) has become a highly researched area. In this contribution, a new and minimalist method is successfully employed to accomplish fully oxygen-tolerant reversible addition-fragmentation chain transfer (RAFT) polymerization using bis(trithiocarbonate) disulfides (BisTTC) as an iniferter agent, where the released sulfur-centered trithiocarbonate (TTC) radical can initiate monomer. Furthermore, polymerization kinetics revealed the typical "living" features of this polymerization system. More importantly, by high-throughput screening, it is found that dodecyl-substituted TTC is responsible for the fully oxygen-tolerant RAFT polymerization though trithiocarbonate radical initiation and R radical deoxygenation. It is believed that trithiocarbonate radical initiation strategy provides a powerful and minimalist tool for fully oxygen-tolerant RDRPs.

Constructing Three-Dimensional Architectures to Design Advanced Copper-Based Current Collector Materials for Alkali Metal Batteries: From Nanoscale to Microscale.

Alkali metals (Li, Na, and K) are deemed as the ideal anode materials for next-generation high-energy-density batteries because of their high theoretical specific capacity and low redox potentials. However, alkali metal anodes (AMAs) still face some challenges hindering their further applications, including uncontrollable dendrite growth and unstable solid electrolyte interphase during cycling, resulting in low Coulombic efficiency and inferior cycling performance. In this regard, designing 3D current collectors as hosts for AMAs is one of the most effective ways to address the above-mentioned problems, because their sufficient space could accommodate AMAs' volume expansion, and their high specific surface area could lower the local current density, leading to the uniform deposition of alkali metals. Herein, we review recent progress on the application of 3D Cu-based current collectors in stable and dendrite-free AMAs. The most widely used modification methods of 3D Cu-based current collectors are summarized. Furthermore, the relationships among methods of modification, structure and composition, and the electrochemical properties of AMAs using Cu-based current collectors, are systematically discussed. Finally, the challenges and prospects for future study and applications of Cu-based current collectors in high-performance alkali metal batteries are proposed.

The effects of BMP2 and the mechanisms involved in the invasion and angiogenesis of IDH1 mutant glioma cells.

PURPOSE: This study investigated the effect of an isocitrate dehydrogenase 1 (IDH1) mutation (mutIDH1) on the invasion and angiogenesis of human glioma cells. METHODS: Doxycycline was used to induce the expression of mutIDH1 in glioma cells. Transwell and wound healing assays were conducted to assess glioma cell migration and invasion. Western blotting and cell immunofluorescence were used to measure the expression levels of various proteins. The influence of bone morphogenetic protein 2 (BMP2) on invasion, angiogenesis-related factors, BMP2-related receptor expression, and changes in Smad signaling pathway-related proteins were evaluated after treatment with BMP2. Differential gene expression and reference transcription analysis were performed. RESULTS: Successful infection with recombinant lentivirus expressing mutIDH1 was demonstrated. The IDH1 mutation promoted glioma cell migration and invasion while positively regulating the expression of vascularization-related factors and BMP2-related receptors. BMP2 exhibited a positive regulatory effect on the migration, invasion, and angiogenesis of mutIDH1-glioma cells, possibly mediated by BMP2-induced alterations in Smad signaling pathway-related factors.After BMP2 treatment, the differential genes of MutIDH1-glioma cells are closely related to the regulation of cell migration and cell adhesion, especially the regulation of Smad-related proteins. KEGG analysis confirmed that it was related to BMP signaling pathway and TGF-ß signaling pathway and cell adhesion. Enrichment analysis of gene ontology and genome encyclopedia further confirmed the correlation of these pathways. CONCLUSION: Mutation of isocitrate dehydrogenase 1 promotes the migration, invasion, and angiogenesis of glioma cells, through its effects on the BMP2-driven Smad signaling pathway. In addition, BMP2 altered the transcriptional patterns of mutIDH1 glioma cells, enriching different gene loci in pathways associated with invasion, migration, and angiogenesis.

Returning to pre-injury level of sports before 9 months after medial patellofemoral ligament reconstruction increases the incidence of anterior knee pain in young patients.

PURPOSE: This study aimed to identify the risk factors for anterior knee pain (AKP) after medial patellofemoral ligament reconstruction (MPFLR). METHODS: Patients aged 15-35 years who underwent isolated MPFLR between 2012 and 2022 were included in the study. These patients were divided into two groups (AKP and control group). Patient demographics and preoperative and postoperative clinical outcomes between the two groups were assessed and compared. Univariate logistic regression analysis was performed to explore the potential risk factors associated with postoperative AKP. Subgroup analysis stratified the results based on the time to return to sports (RTS) (>9 and ≤9 months). Furthermore, Spearman correlation analysis was performed to investigate the association between Kujala score and time to RTS. RESULTS: A total of 206 patients were included (AKP, n = 59; control, n = 147). At the 2-year follow-up, patients with AKP demonstrated a shorter duration in returning to their pre-injury activity level compared to those without AKP (9.0 ± 3.6 vs. 10.3 ± 2.7 months, p < 0.05). RTS earlier than 9 months after MPFLR was the only significant risk factor associated with postoperative AKP (odds ratio, 2.13, 95% confidence interval, 1.03-4.39; p < 0.05). Further subgroup analysis revealed that patient RTS earlier than 9 months exhibited worse patient-reported outcomes in both the total cohort and control group (p < 0.05). Furthermore, among patient RTS within 9 months, a longer recovery duration before RTS strongly correlated with a higher Kujala score (R = 0.670, p < 0.001). CONCLUSIONS: Young patients who RTS at their pre-injury levels before 9 months after MPFLR have a higher incidence of postoperative AKP and poorer functional outcomes compared to those who delay their return. Specifically, within the first 9 months after MPFLR, the earlier the RTS, the more severe the AKP symptoms. Careful consideration of the timing for RTS may help reduce the incidence of postoperative AKP. LEVEL OF EVIDENCE: Level III.